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Distributive Property is a mathematical concept that is relevant in various fields, including machine learning. The Distributive Property describes how operations (usually addition and multiplication) distribute over each other. In machine learning, it can be applied when working with mathematical expressions, linear algebra, and various operations involving vectors, matrices, and scalars. 

The Distributive Property for multiplication over addition is expressed as follows: 

 a × (b + c) = a × b + a × c ------------------------------------------- [3625a]

For matrices, the Distributive Property is also relevant, considering matrix multiplication and addition: 

A × (B + C) = A × B + A × C ---------------------------------------- [3625b]

where,

  A, B, and C are matrices, and the operation (B+C) is performed element-wise. 

In machine learning, we may encounter the Distributive Property when dealing with linear transformations, feature scaling, or the application of mathematical operations to datasets. 

De Morgan's Laws, along with the Distributive Property, can be extended to many complex logical formulas, for instance:

 A ∧ (B ∨ C) ≡ (A ∧ B) ∨ (A ∧ C) --------------------------------------- [3625c]

A ∨ (B ∧ C) ≡ (A ∨ B) ∧ (A ∨ C) --------------------------------------- [3625d]

(P ∧ Q) ∨ (R ∧ S) ≡ (P ∨ R) ∧ (P ∨ S) ∧ (Q ∨ R) ∧ (Q ∨ S)  --------------------------------------- [3625e]

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